nemo-model-selection

name	nemo-model-selection
description	Recommends an LLM for a NeMo Platform agent based on what the agent actually has to do, explained in plain English before any benchmark name appears. Use when the user is choosing a model for a new agent, asking which model to use, or unsure what to put in their spec or NAT workflow YAML. Invoked by nemo-explore at the model question; also runs standalone when the user starts mid-flow.
triggers	["which model should I use","what model is best for this","help me pick a model","recommend a model","I don't know what model to use","model selection","which LLM"]
not-for	["nemo-explore (use first to capture the agent's job, audience, and tools)","nemo-spec (use to persist the design once model is chosen)","nemo-build-agent (use to scaffold the YAML once the spec is signed off)"]
compatibility	nemo-platform >= 0.1.0; read-only; loads references/benchmark_cache.json if present; works offline; safe under any sandbox.
maturity	active
license	Apache-2.0
user-invocable	true
allowed-tools	["Read","Bash"]

NeMo Platform model selection

Recommends a model for a new agent (NIM or any other provider configured on the running platform). Plain-English first, benchmark numbers second, never the other way around. Output: one recommended model with a one-sentence reason, ready to drop into a spec or NAT workflow YAML.

Pre-flight

1. Load the benchmark cache

test -f packages/nemo_platform_ext/src/nemo_platform_ext/skills/nemo-model-selection/references/benchmark_cache.json && echo "cache_present" || echo "cache_missing"

If cache_missing, proceed with the static table in this file. Tell the user once that benchmark data is stale and they can refresh it with:

python scripts/refresh-benchmark-cache.py

The cache (schema v6+) carries four things the rest of this skill reads:

• models[] — editorial entries for a curated set of NIMs with strong_at, watch_out_for, intent_hints, derived_from lineage, and direct/inferred scores.
• upstream_index.bfcl_v4 and upstream_index.arena_elo — full BFCL and per-category Arena Elo tables for ~84 and ~360 models respectively. Use these to look up scores for ANY model name, not just the registered ones.
• namespace_to_type[] — namespace-prefix → NAT _type value mapping for the YAML emitter.
• name_decomposition_rules[] — pattern→hint rules for synthesizing intent_hints when an unknown model name lands.

2. Fetch the live model list from the running platform

curl -fsS http://localhost:8080/v1/models 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); names=[m.get('id') for m in d.get('data',[])]; print('\n'.join(n for n in names if n))" 2>/dev/null || echo "PLATFORM_UNREACHABLE"

Interpretation:

• List of model ids returned → these are the candidates the user can actually pick from. Carry them through to Step 1+.
• PLATFORM_UNREACHABLE → platform isn't up. Fall back gracefully: tell the user "I can't reach the local platform, so I'll recommend from the curated NIM set in the cache instead of your actual available models. Start the platform with nemo services run if you want recommendations grounded in what's deployed."

The /v1/models response is OpenAI-shaped ({data: [{id, ...}, ...]}); the parse above extracts the id field per entry. If the platform's response shape differs, adjust the parse but keep the failure mode (graceful fallback to closed registry, never silently steer the user).

Step 0 — Pick the conversation direction

Before the profile questions, ask which path the user is on:

Quick check before I ask the design questions:

A. **You're choosing a model.** Walk me through what the agent does and I'll
   recommend from what's deployed.
B. **You already have a model in mind.** Tell me which one — I'll assess
   whether it fits your task and surface what to watch for.

Which are we doing? (If unsure, A is the default.)

If the user picks A → continue to Step 1 with the recommend flow. If the user picks B → continue to Step 1 with the assess flow:

• Skip the "recommend a model" framing entirely in Step 2.
• Still ask the three profile questions in Step 1 (you need them to evaluate fit).
• In Step 2, produce a fit assessment for the user's named model rather than a recommendation — same evidence-quality flagging, but the output frames as "here's what we know about for " not "use this model."

Step 1 — Profile the agent

Ask all three questions in a single message. Skip any that the conversation has already answered (for example, nemo-explore already captured tools and deployment).

Before I recommend a model, three quick things about what the agent will do:

1. **Tool density.** How many tools, and how do they interact?
   - One tool (search, lookup, or similar)
   - 3–5 tools whose outputs chain into each other
   - Many tools, often called in parallel

2. **Primary capability.** What does the model spend most of its time doing?
   - Calling APIs or tools reliably (MCP, structured function calls)
   - Working with code (reading repos, editing, writing patches)
   - Reading and reasoning over long documents
   - General conversation and instruction following

3. **Deployment.**
   - Cloud (NVIDIA Build API, OpenAI, Anthropic, etc.)
   - Self-hosted on a GPU — if so, roughly how much VRAM is available?
   - Not decided yet

Do not propose a model before all three answers are in. Push back on "you decide" — commit to a default and announce it ("I'll assume cloud and a tool-heavy agent. Tell me if that's wrong.").

Step 1.5 — Build the candidate set and pick a presentation pattern

Building candidates

The candidate set is what the user can actually pick from. It comes from three joins:

1. Start with the pre-flight model list from /v1/models (or, if PLATFORM_UNREACHABLE, fall back to the cache's models[] editorial registry).
2. For each available model id, look up evidence in this order:
   • Token-match against the editorial models[] entries → if hit, use the full editorial record (lineage, intent_hints, direct + inferred scores)
   • If no editorial match, token-match against upstream_index.bfcl_v4 keys → if hit, use that BFCL score with source: "direct_external"
   • Same for upstream_index.arena_elo for per-category Elo
   • If neither editorial nor upstream matches, synthesize intent_hints by walking name_decomposition_rules[] and collecting every hint whose pattern token appears in the decomposed model id. Mark evidence as source: "name_only".
3. Rank candidates by the user's profile — primary capability axis determines which score field dominates.

When /v1/models was unreachable, also tell the user the rest of this flow is operating on the curated NIM set, not their actual deployment.

Picking the presentation pattern

The pattern depends on the evidence quality of the candidate that best matches the profile:

Identify the best-matching candidate.
Inspect its evidence for the primary axis from profile question 2:
  - "code"               → scores.arena_elo.coding  AND  scores.bfcl_v4
  - "tools"              → scores.bfcl_v4
  - "long documents"     → scores.arena_elo.hard_prompts
  - "general/instruction"→ scores.arena_elo.overall AND scores.arena_elo.instruction_following

If the relevant scores all have source ∈ {"direct", "direct_external"}:
  → Pattern A (single recommendation, current Step 2 template)

If one or more relevant scores have source == "inferred_from_ancestor",
or the candidate has scores == {} (name-only) but has intent_hints:
  → Pattern B (forced trade-off, withhold model name until user resolves it)

If 3+ candidates have similar profiles and similar evidence-quality:
  → Pattern C (shortlist with explicit "I'd want an eval before deciding")

Pattern B — forced trade-off

Hard rule: when the primary candidate's evidence is anything other than direct, the model name does NOT appear in your response until the user has resolved the trade-off. This is the anchoring guard. Without it, users default to the first model named regardless of caveats.

Structure:

1. State that there's a trade-off to settle before naming a model.
2. Present exactly two candidates labeled A and B (NOT named):
   • A. The matched specialist — the candidate that best fits the profile by name/intent, with its inferred/missing-data caveat.
   • B. The measured generalist — the model with the strongest direct score on the same primary axis, even if less specialized.
3. For each, summarize the plain text from the cache (which already carries the editorial caveat for inferred entries) in your own words.
4. Recommend a 5–10 prompt eval from the user's spec on both as the "real answer."
5. If the user can't run an eval, ask: which framing of your priority is right — A's specialist intent or B's measured discipline?
6. Only after the user picks — name the chosen model and proceed to Step 2's recommendation template (skipping the model-selection paragraph since it's resolved).

Example output for a code-heavy agent where the natural pick has inferred BFCL data:

"Before I name a model — one thing to settle. Your profile (code-focused, three tools, cloud) has two reasonable candidates with different evidence quality:

A. The specialist. Built for this exact job. Arena's coding-category Elo (1418, strong) IS measured for this exact model. But BFCL hasn't measured its tool-calling discipline — only its base model (Qwen3-30B-A3B at 37%) and we're inferring from there.

B. The measured generalist. Best directly-measured tool-calling in our NIM set (BFCL 52%, mid). Not code-specialized, but disciplined.

If you can run a 5–10 prompt eval from your spec on both, that's the real answer. If you need to commit now: which matters more — specialist intent (A) or measured discipline (B)?"

Pattern C — shortlist (rare)

Use only when 3+ candidates have similar profiles and similar evidence-quality, and no honest trade-off binary exists. Present them in a numbered list with plain summaries and explicit phrasing that an eval is the only way to actually decide. Do not commit to one. Default to recommending an eval as the next step.

Step 2 — Recommend in plain English

Lead with the capability that matters most for this agent, then name the model as the conclusion. Use this shape (adapt the content, do not copy verbatim):

"For an agent that , the thing that matters most is <one capability in plain words — e.g. 'reliably picking the right tool without hallucinating its arguments'>. That's what is built for — <one sentence on what the model actually does well, not what it scored>. The tradeoff is . Given you're deploying <cloud / self-hosted>, <model string> is the practical choice."

Recommendation table

Agent profile	Recommended model
Many tools, parallel or chained calls	qwen/qwen3-235b-a22b
Many tools, cost or speed is a constraint	qwen/qwen3-30b-a3b
Code-focused: reading repos, writing patches	qwen/qwen3-coder-30b-a3b-instruct
Reasoning over long documents or many chunks	nvidia/llama-3.1-nemotron-ultra-253b
General-purpose, balanced default (platform-curated)	nvidia/llama-3.3-nemotron-super-49b-v1
General-purpose, widely-deployed alternative	meta/llama-3.3-70b-instruct
Fast and cheap, simple tool loops	microsoft/phi-4-mini-instruct
Self-hosted, single consumer GPU (<24 GB)	qwen/qwen3-8b

What each model is strong at

If benchmark_cache.json is present, read each model's strong_at and watch_out_for from the cache — those are kept in sync with the script. The text below is the fallback when the cache is missing.

qwen/qwen3-235b-a22b

• Correctly calls multiple tools in a single turn without mixing up arguments
• Handles parallel tool invocation where call order matters
• Recovers gracefully when a tool returns an error instead of hallucinating a result
• Watch out for: heavy VRAM footprint for self-hosting; overkill for single-tool agents

qwen/qwen3-30b-a3b

• High-throughput tool calling at low inference cost (sparse MoE, ~3B active params)
• Good baseline before you know whether you need the larger model
• Watch out for: lower ceiling on complex nested tool chains

qwen/qwen3-coder-30b-a3b-instruct

• Navigates unfamiliar codebases and makes targeted edits
• Agents that interact with git, CI, or code-review workflows
• Watch out for: general reasoning suffers from the specialization

nvidia/llama-3.1-nemotron-ultra-253b

• Follows threads across very long documents without losing context
• Multi-step reasoning over dense technical material
• Watch out for: slower inference; cloud API is the practical deployment path

nvidia/llama-3.3-nemotron-super-49b-v1

• The platform's curated default for cloud agents — well-tested across the build path
• Balanced: solid tool-calling, solid prose, no glaring weakness
• Watch out for: a specialist will beat it on heavy tool chains or hard code tasks

meta/llama-3.3-70b-instruct

• Reliable across a wide range of tasks with well-characterized behavior
• Good starting point when you don't yet know where the bottleneck will be
• Watch out for: not a specialist — pick one if tool-calling or code quality is critical

microsoft/phi-4-mini-instruct

• Fast, cheap inference for latency-sensitive agents or high-volume loops
• Fits on small hardware for edge or resource-constrained deployments
• Watch out for: lower ceiling for complex multi-tool orchestration

qwen/qwen3-8b

• Tool calling on a single consumer GPU (fits in 12–16 GB VRAM)
• Best choice for local development and prototyping
• Watch out for: not competitive on complex reasoning; context reliability drops faster

Step 3 — Mention the evidence only after the plain-English case

After explaining the recommendation, briefly note how we know — framed as "the test that simulates this" not "the score it got".

Right framing:

"We know this because it consistently outperforms comparable models on tests that simulate exactly this kind of multi-tool coordination — not toy examples, but real scenarios where the model has to decide which tool to call, in what order, with what arguments, and when to call nothing at all."

Wrong framing (do not do this):

"It scored 0.91 on BFCL v4, which is top tier, and 1342 Arena Elo."

If the user asks what benchmark was used or wants the raw number, tell them. Do not lead with it.

Step 4 — Output

Two ready-to-paste blocks. Show whichever fits the user's stage. When the chosen model's primary-axis score has source: "inferred_from_ancestor" or the model relies on intent_hints only, include an explicit evidence caveat in the output — don't let the spec or YAML carry the recommendation forward without surfacing the inference.

If they're authoring an agent spec for nemo-spec:

## Model

- **Family/size:** <plain description, e.g. "Qwen3 235B MoE">
- **NIM model id:** `<model-string>`
- **Why this choice:** <one plain-English sentence — same words you used above>
- **Evidence:** <"Direct BFCL and Arena measurements" | "BFCL inferred from ancestor <ancestor-id>; Arena measured directly" | "No public benchmark coverage — selection based on model-name intent only, eval recommended">
- **Deployment:** <cloud | self-hosted (VRAM)>

If they're editing a NAT workflow YAML directly (e.g. tweaking the agent.yml nemo-build-agent produced):

llms:
  primary_llm:
    _type: <nat-type>
    model_name: <model-string>
    # Chosen because: <one plain-English sentence — same words you used above>
    # Evidence: <direct | inferred from <ancestor-id> | name-intent only — eval recommended>
    max_tokens: 4096

workflow:
  _type: <agent_type>
  llm_name: primary_llm
  tool_names: []

Picking the right _type

Match the chosen model's namespace prefix against namespace_to_type[] from the cache:

For each rule in cache.namespace_to_type:
  if model_string.startswith(rule.prefix):
    use rule.nat_type for the YAML _type field
    if rule.note is present, surface it once to the user
    stop
If no rule matches:
  Tell the user the namespace is unrecognized. Run this to confirm the right type:
    uv run nat info components -t llm_provider
  Until the user provides the type, leave _type as <TBD-verify> in the emitted YAML
  rather than guessing.

Common mappings the cache carries today: nim/*, openai/*, anthropic/*, bedrock/*, plus vendor-published NIM names (qwen/*, meta/*, nvidia/*, microsoft/*, mistralai/*) that route through the NIM provider when served by the platform. Ollama's local endpoint maps to _type: openai since it exposes an OpenAI-compatible API.

When the chosen model is non-NIM, also remind the user to set base_url and api_key (or the equivalent env vars) in the LLM block — those are mandatory for non-NIM providers and aren't auto-filled like they are for the platform's NIM defaults.

Pair the model with the right agent type

What the agent needs to do	Use
Call tools in a loop, observe results, adjust	react_agent
Dispatch tools directly without intermediate reasoning	tool_calling_agent
Reason through a problem before acting	reasoning_agent
Follow a fixed plan with no dynamic re-planning	rewoo_agent

Step 5 — Staleness notice

End with:

These recommendations reflect benchmark data current as of this skill's last
cache refresh. LLM rankings shift often. To pull fresh scores:

    python scripts/refresh-benchmark-cache.py

Raw leaderboards:
  Tool calling:        https://gorilla.cs.berkeley.edu/leaderboard.html
  Human preference:    https://lmarena.ai
                       (live Elo data: huggingface.co/datasets/lmarena-ai/leaderboard-dataset)

Verification

This skill writes nothing. Verification is conversational: summarize the recommendation in 3 lines (capability that mattered most, model chosen, one tradeoff) and ask "Does this match what you need?" Do not hand off until the user confirms.

If nemo-explore invoked this skill, return control to nemo-explore with the chosen model so it can continue to the constraints question. If the user invoked standalone, hand off to nemo-spec if they want to persist the design.

If verification fails

Symptom	Cause	Recovery
User says "wrong recommendation"	One of the three profile answers was misread	Re-ask just that question; do not restart
User wants a model not in the table	The table is curated, not exhaustive	Tell them honestly; describe the capability gap their choice would have vs the closest recommended model
cache_missing and user wants fresh data	Cache has never been refreshed in this checkout	Tell them the refresh command and note that the static table is still usable
User picks self-hosted but the recommended model needs cloud	Hard constraint conflict	Drop the recommendation; pick the closest self-hosted-compatible model from the table

Hard rules

• Never name a model before all three profile questions are answered.
• Never lead with a model name, benchmark name, or score.
• Never recommend a cloud-only model when the user said self-hosted.
• Never write model_name (YAML) or "NIM model id" (spec) without showing the plain-English reason alongside it.
• When the primary candidate's evidence is anything other than direct, the model name does not appear in your response until the user has resolved the trade-off in Pattern B. Anchoring is the failure mode this guards against — users default to the first model named regardless of caveats. The withhold is non-negotiable.
• When emitting the spec or YAML block, always include an Evidence line/comment naming the source quality. Inferred or name-only choices that propagate downstream without that signal mislead the build skill and the user both.

Gotchas

• "You decide" needs a committed default, not a silent fill-in. Same rule as nemo-explore. Pick something, name it, tell the user.
• The platform default is nvidia/llama-3.3-nemotron-super-49b-v1. If nemo-explore already captured "cloud, no preference", you can route there without re-profiling — but still explain why in plain English instead of just naming it.
• Two model name formats coexist. Entity-name with hyphens for NAT YAML / nemo chat / nemo agents. API-Catalog format with slashes for Data Designer. Use the slashed form (qwen/qwen3-235b-a22b) in NAT YAML for cloud NIMs; the build skill converts when needed.
• Watch the deployment column. A 235B cloud-API recommendation aimed at a self-hoster with a 24 GB GPU is the most common mismatch and the easiest to catch by re-reading Step 1.